Regulation of wine flavour complexity through canopy height and harvest timing synergy

Exploring the nutritional, color, and sensory profiles of Madeira Island avocados

Optimizing plant biomass recovery from constructed wetlands within the water-energy-food nexus: Influence of harvesting time on biogas production

Impact of harvest timing and ensiling on methane yield of Arundo donax L.

Tomatoes (Solanum lycopersicum) are not only a staple in cuisines worldwide but also a subject of scientific interest due to their health benefits and distinct ripening process. Recognizing the ripest and most flavorful tomatoes has led to innovative research combining technology and agriculture. In this context, image processing emerges as a promising tool to discern the quality of tomatoes, particularly through color analysis. This study explores the effectiveness of a region-based image processing system in identifying red, ripe tomatoes. Currently, this process is done by hand, which takes time and can lead to mistakes-developed a machine learning-based device that utilizes computer vision and image processing techniques to detect ripe tomatoes with high accuracy. By employing algorithms that analyze color, texture, and shape, our technology can identify the optimal harvest time, making the process faster, more efficient, and more cost-effective. Automating tomato harvesting is crucial to addressing the labor crisis and enhancing the effectiveness of the present harvesting process. The actualization of automated harvesting depends on the ability to precisely recognize fruits. Fruit that is harvested at its peak maturity has the maximum levels of taste, vitamins, and sale value, which optimizes financial gains. There is now an inadequate rate of identification and failure to identify because of the blockage of specific fruits by vegetation and unwanted fruits, as well as the color change brought on by light. In order to identify tomato fruits in difficult circumstances, this research suggests a tomato identification system using the enhanced YOLOv8 framework. According to the model's test evaluation, the YOLOv8-Tomato model's mAP0.5 was 86.9%, its recall rate was 98%, and its accuracy and precision were 94% and 90%, respectively.

Royal jelly is produced by young worker honeybees that support the growth of their larvae. Human beings can harvest and use it as functional food due to its high content of nutrients and beneficial human health effects. This study aims to investigate how harvesting time affects sugar and minerals, B vitamins profiles, and antioxidant (free radical scavenging) activity. Royal jelly samples were collected at third and sixth days of secretion and deposition by honeybees (Apis mellifera) in Holeta Bee Research Center, Ethiopia. Sugar, minerals and B-vitamins profiles and radical scavenging potential were examined by using standard methods. The highest sugar level was fructose (0.48 ± 0.03%) and the lowest was maltose (0.05 ± 0.01%) harvested on the third and sixth days, respectively and significant difference (ρ < 0.05) among fructose and sucrose. The minerals concentrations in descending order are K (4144.32 ± 174.98 mg/kg)> Mg (309.61 ± 8.33 mg/kg)> Ca (235.26 ± 1.05 mg/kg)> Na (155.60 ± 1.76 mg/kg) and the most abundant elements in royal jelly and significantly vary (ρ < 0.05) based on harvesting time, except in Mg. The highest vitamin content was vitamin B9 (14.8 ± 0.07 mg/kg) and the lowest was vitamin B3 (0.57 ± 0.06 mg/kg) harvested on third and sixth days, respectively and significant difference (ρ < 0.05). Royal jelly exhibited the strongest free radical scavenging activity (75.38%) and the lowest (59.97%), collected from the third and sixth days respectively and significant difference (ρ < 0.05). The inhibition concentration (IC50) value of royal jelly collected on third and sixth was 4.84% and 6.56%, respectively. Thus, the evaluated nutritional components and antioxidant properties in the royal jelly altered through harvesting times, and varied between royal jelly collected at different times, and found that royal jelly collected at third days more nutritious.

The wild passion fruit (Passiflora cincinnata Mast.) is an edible fruit with a great agro-industrial potential. Its peel is highly nutritious; however, it is often discarded as waste. This study aimed to produce and evaluate the physicochemical and nutritional properties of flour made from the peel of wild passion fruit ‟Cerro Corá”. The fruit was grown under both irrigated and rainfed conditions and harvested at different times. The completely randomized factorial design (2×3) comprised two cultivation systems (irrigated and rainfed) and three harvest times (60, 80, and 100 days after anthesis). The moisture content and water activity of the flours did not depend on the cultivation system. The greatest preservation of phenolic compounds was observed in the samples grown under rainfed conditions. The best physicochemical profile and bioactive content belonged to the flour samples produced 80 days after anthesis. These flours can be used as ingredients for functional food products due to their antioxidant potential.

The article presents the results of research on the effect of agrochemical agents used in the crop hemp agrocenosis to protect against weeds on the contamination and economically useful signs of cultivated plants. The experiments were conducted in the Penza region on technical hemp of the Central Russian ecotype of the Nadezhda variety in 2021–2024. The biological effectiveness of protective measures when sprayed with Lontrel Grand and Miura herbicides was 59–77 and 54–86%. With the combined effect of all the factors studied, this indicator was within the range of 64–89 and 77–98%, respectively. Treating seeds with the substances that have growth-stimulating properties allowed for an increase in field germination by 5–7%. Agrochemicals used in various weed control methods have increased the survival rate of hemp plants by 10–16% by harvest time. The weakening of competition between cultivated and weed plant shad a significant positive effect on morphometric parameters. Seed treatment contributed to an increase in the length of the inflorescence by 6–10%, leaf top dressing – by 3.7%. Moreover, spraying the leaves with Izagri Vita caused an increase in stem diameter by 2.4%. The formation of a high increase in the yield of stems (1.47 and 1.50 t/ha) occurred due to herbicide weeding with the studied preparations against the background of treatments of seed material (Artafit, AgroVerm Ekran) and vegetative plants (Izagri Vita). The presented protection scheme, which includes seed treatment and fertilization with a liquid complex fertilizer per leaf, depending on the type of herbicide (against dicotyledonous or monocotyledonous weeds), provided an increase in seed yield of 0.33–0.40 and 0.29–0.41 t/ha.

ABSTRACT Mixed cereal forages offer opportunities to balance forage yield, nutritional quality, and extend the grazing season for beef cattle. This two-year study evaluated nine spring-planted cereal mixtures and five monocrops in a randomized complete block design. Forage dry matter yield (FDMY) and quality data were collected. Significant differences (p < 0.05) in FDMY were observed, with monocrop soft white wheat (Triticum aestivum L.) (SWW), monocrop triticale (x Triticosecale Wittmack), and barley (Hordeum vulgare L.) – triticale mixture producing the highest yield, while monocrop fall rye (Secale cereale L.) yielded the lowest. On average, the mixtures generally had approximately 1% higher crude protein (CP) compared with monocrops (13% vs. 12%; p < 0.05). Fall rye differed significantly from nine of the treatments, including other monocultures. Compared with monocrops of oats (Avena sativa L.), spring triticale and SWW, the inclusion of barley in the mixtures generally increased CP by 1–5%. The FDMY and CP were negatively correlated in monocrops but positively correlated in mixtures, indicating mixtures can partially decouple the classical yield–quality trade-off. Principal component analysis captured 63.2% of the variation of the first two components, separating treatments into functional clusters: high-yielding triticale/SWW, fiber-rich oat/fall rye mixtures, and barley-dominant mixtures with higher relative feed value and digestible energy. Barley–oat mixtures exhibited intermediate traits, suggesting opportunities to optimize nutritional outcomes through seeding ratios, harvest timing, and variety selection. These results support targeted mixture selection to meet beef cattle nutritional requirements and indicate cereal mixtures could provide a practical option for forage-based livestock production.

Accurate crop yield prediction is fundamental to sustainable agricultural management, enabling optimized resource allocation and informed decision-making. However, a critical gap exists in current prediction models: existing approaches overlook the temporal alignment between meteorological conditions and production management phases—defined as the intervals between consecutive agronomic operations (e.g., sowing, fertilization, thinning). This oversight results in suboptimal predictive performance, as conventional whole-season weather aggregation fails to capture phase-sensitive crop–weather interactions. While machine learning (e.g., XGBoost) and deep learning approaches (e.g., CNN, LSTM) have been applied to yield prediction, these models typically treat weather variables as temporally homogeneous inputs, inadequately modeling the correlation between historical yields and phase-specific meteorological patterns. To address this gap, this study proposes CNN-LSTM-AM, an innovative hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM), and attention mechanisms (AMs), utilizing weather data explicitly aligned with production management phases as input. The CNN component extracts cross-phase weather patterns, the LSTM captures sequential dependencies across growth stages, and the attention mechanism dynamically weights phase importance based on meteorological conditions. The proposed model is validated using a real-world case study of Bok choy production from an agricultural cooperative in Yunlin County, Taiwan, encompassing 1714 production cycles over eight years (2011–2019). Experimental results demonstrate that CNN-LSTM-AM achieves an RMSE of 1448.24 kg/ha, MAPE of 3.60%, and R2 of 0.98, outperforming five baseline models—CNN (RMSE = 2919.18), LSTM (RMSE = 2529.74), CNN-LSTM (RMSE = 1516.44), LSTM-AM (RMSE = 2284.64), and XGBoost (RMSE = 3452.47)—representing a notable reduction in prediction error (58% lower RMSE) compared to XGBoost. Furthermore, prediction accuracy improves progressively as harvest time approaches, and phase-specific weather encoding enhances accuracy by 16.5% compared to whole-season averaging. These findings underscore the critical importance of integrating agronomic domain knowledge into data-driven prediction frameworks.

ABSTRACT In this study, 48 genuine absolute samples of Egyptian J. grandiflorum across three harvest seasons (2022–2024) were analysed using gas chromatography coupled to mass spectrometry and flame ionization detector (GC-MS/FID), alongside sensory evaluation to assess variations linked to harvest times. Identification of volatile compounds and sensory analysis revealed seasonal differences. Principal component analysis (PCA) further highlightedvolatile markers that differentiated sample subsets according to their seasonal profiles. Given jasmine absolute’s high commercial value, authenticity assessment is crucial to avoid possible adulteration. In this study, for the first time, enantioselective multidimensional GC coupled to isotope ratio mass spectrometry (MDGC-C-IRMS/qMS) was applied to jasmine absolute, enabling simultaneous determination of enantiomeric ratios and δ 13C isotopic signatures within a single analytical workflow. Furthermore, typical δ 13C isotopic signatures were registered for (-) and (+) linalool and indole across the seasons, providing potential novel markers for jasmine absolute authenticity, and quality control.

Sweet potato (Ipomoea batatas L.) is a vital root crop valued for its nutritional quality, yield potential, and adaptability; however, the effects of early harvesting on yield, root size distribution, and marketable value are not well understood. This study evaluated nine genotypes to compare genotype performance at two harvest times, classify roots by economic value, and assess consumer-preferred traits. The experiment was conducted at 90 and 120 days after transplanting (DAT). Morphological, yield, and nutritional traits were measured, including SPAD value, vine and leaf characteristics, shoot weight, root number and mass, economic root number and mass, soluble sugars, and total yield. Roots were classified into six size classes based on mass and number. More than 40% of root mass per plot consisted of roots weighing 30-100g, with the remainder distributed among five other size classes. Genotypes G-89 and BARI Sweetpotato-15 produced 150% and 65% higher yields at 120 DAT compared with 90 DAT. At 120 DAT, BARI Sweetpotato-17, G-193, G-89, and BAU Sweetpotato-5 produced roots across all six size classes. Sensory evaluation at 90 DAT indicated that G-54 and G-193 were preferred for texture, boiling, and baking, while G-89 and BAU Sweetpotato-5 were favored for aroma, indicating potential traits associated with earlier maturity under the study conditions. Genotypes G-89, G-184, BARI Sweetpotato-15, and G-138 exhibited comparatively higher yields and larger storage roots at 120 days after planting (DAT) under char land conditions, while several other genotypes including G-54 produced satisfactory yields and acceptable root quality at 90 DAT. This indicates their comparatively earlier performance within the evaluated harvest times under the specific char land agro-ecological conditions of the study. Broader applicability and definitive classification would require further validation across additional harvest stages and agro-ecological zones.

Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, valued for its bioactive constituents and associated health benefits. This study evaluated the influence of four agronomic and processing factors-harvest month, destoning, fruit washing, and bottling delay-on the chemical composition of Kolovi EVOOs from the PGI Lesvos region. A total of 34 oils were produced under standardized conditions and analyzed for phenolic compounds, tocopherols, pigments, and squalene using UPLC-QTOF-MS and HPLC-DAD. The oils were characterized by consistently high nutritional quality, with most samples fulfilling EFSA health claim thresholds for hydroxytyrosol, tyrosol and its derivatives, and α-tocopherol. Harvest month was the most influential parameter: early harvested oils (October) contained significantly higher levels of phenolics, α-tocopherol, and lutein, whereas later harvests (November) were richer in squalene. Destoning produced modest changes, with slightly higher phenolics in non-destoned oils and reduced lipophilic antioxidants in destoned samples. Fruit washing selectively decreased hydrophilic phenolics, while lipophilic compounds were largely unaffected. Bottling delays of up to 48 h under protective conditions had negligible effects on composition, aside from minor increases in specific phenolic derivatives. These findings suggest that early harvesting and careful consideration of destoning are the most effective strategies for supporting the antioxidant profile of Kolovi EVOOs, while other practices can be adjusted with limited impact on quality.

Determining the optimal harvest time is among the most critical economic decisions for peanut (Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide.

Climate change has driven the expansion of viticulture in high-altitude tropical regions. The dynamics of grape ripening and its metabolic profile are influenced by climatic conditions. This study aimed to identify the optimal harvest period for Cabernet Sauvignon grapes cultivated in an emerging Brazilian high-altitude region using a targeted metabolomics approach. Phenolic composition, antioxidant activity, and physicochemical parameters were monitored over a 10-week ripening period from veraison. Grapes harvested between weeks 6 and 9 exhibited physicochemical attributes favorable to winemaking, including pH (3.50-3.63), soluble solids (21.33-21.43 °Brix), skin hardness (4.52-6.23 kg m s-2), berry firmness (157.83-177.62 kg m s-2), and color. Grapes from 6 and 7 weeks presented the highest levels of anthocyanins (delphinidin-3-glucoside, cyanidin-3-glucoside, petunidin-3-glucoside, malvidin-3-glucoside, among others), flavan-3-ols (epicatechin and epicatechin gallate), and flavonols (myricetin-3-O-glucuronide, myricetin-3-O-glucoside, and kaempferol-3-O-glucoside). These metabolites contribute to color enhancement and stability through copigmentation, accounting for the elevated antioxidant activity observed at these stages. Therefore, weeks 6 and 7 represent the ideal harvest period for Cabernet Sauvignon grapes in this high-altitude tropical region, combining favorable physicochemical maturity with maximal phenolic content and antioxidant potential. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Microalgae, being able to produce a variety of bioactive compounds, represent a promising resource for numerous industrial applications. However, their large-scale production remains constrained by biological, technical and economic factors. Open ponds, which are predominantly employed on an industrial scale, yield lower levels of algae in comparison to those obtained in closed reactors. Consequently, the processing of substantial volumes is necessitated during the harvesting process. This study explores the potential of microfiltration as an alternative to conventional harvesting processes to optimise yields and preserve biomass quality. The evaluation of various ceramic membranes, including new-generation prototypes, was conducted according to several operating parameters (flux, backwash mode, recirculation rate). The objective was to obtain microalgae concentrate while preserving cell integrity. Three species (Odontella aurita, Phaeodactylum tricornutum and Dunaliella salina) were considered for issues directly related to industrial cultivation such as seasonality, strain variability and the state of the culture at the time of harvest. An effective cleaning protocol was also developed, applicable to all the conditions tested. The ceramic membranes demonstrated a high degree of resistance to fouling, with their low tortuosity promoting effective backwashing. The membrane process resulted in a high level of cell recovery and volume concentration factors that were comparable to those achieved by conventional methods. In comparison with alternative concentration processes, it is also economically viable, thus confirming its potential as a robust and efficient alternative for industrial-scale microalgae harvesting.

Wireworms, the larval stage of click beetles (Coleoptera: Elateridae), cause substantial damage to potato tubers, particularly during the ripening phase. Management strategies such as early harvest and the use of less susceptible cultivars have been shown to reduce feeding damage. However, the mechanisms driving cultivar susceptibility and the key factors influencing wireworm feeding during ripening remain poorly understood. To address this gap in knowledge, we compared the effects of harvest date, soil moisture, tuber mass, and wireworm abundance on feeding damage of wireworms (Agriotes spp.) during ripening in 2 potato cultivars. Additionally, we investigated whether differences in potato tuber CO2 respiration are responsible for cultivar susceptibility to wireworm damage. Feeding damage significantly varied between cultivars and harvest dates. Early harvest reduced damage in the highly susceptible cultivar but had no effect on the less susceptible cultivar. Soil moisture primarily affected wireworm abundance in the ridge, which decreased under low soil moisture conditions. In the susceptible cultivar, feeding marks increased significantly under low compared to high soil moisture. Tuber mass was positively related to damage, with heavier tubers showing more damage. Differences in tuber CO2 respiration between cultivars neither influenced wireworm feeding nor explained differences in susceptibility. These findings highlight the potential of selecting suitable cultivars and managing soil moisture, harvest timing, and tuber mass to reduce wireworm damage during potato tuber ripening. Understanding the mechanisms of cultivar resistance and breeding more resistant varieties will help minimize wireworm-related crop losses in potatoes.

In biotechnological processes, cell density and physiology are critical parameters for controlling the feed rate, harvest time, and process performance. We developed an automated flow cytometry approach that enables continuous, real-time (fully automated, hourly) monitoring of bacterial populations in continuous bioreactors. The method employed a double-staining protocol that combined DAPI to assess total DNA content and Alexa Fluor 488-EdU via Click-iT technology to identify the proportions of cells undergoing active DNA replication through EdU incorporation. The integrated workflow included fixation, permeabilization, staining, and measurement steps and was applied to three Gram-negative strains: Bradyrhizobium sp., Escherichia coli, and Stenotrophomonas rhizophila. Automated analysis captured growth dynamics and cell cycle progression, providing insights into population behavior under different dilution rates. In this study, automated on-line sampling enabled hourly flow cytometry measurements of cell concentration and physiological indicators during continuous cultivation, supporting real-time monitoring and control in industrial biotechnology.

Rice fields represent man-made semi-aquatic wetlands primed for invasive pests. Monitoring rice field biodiversity using conventional methods, however, is time-consuming and laborious. Environmental DNA (eDNA) methods can provide a fast and effective means to monitor rice field communities and inform management decisions. Our study provides proof-of-concept of rice field eDNA biodiversity assessments, with a focus on native and non-native pests across cultivation phases. We collected eDNA samples from locations in southern Taiwan rice fields during planting and harvesting time, employing eDNA metabarcoding (COI) to detect diverse taxonomic groups. We assigned 77 ASVs across all sites to animal taxa, 34 of which were identified to species. Overall, 18 species were designated as native or non-native (83.3% and 16.6%, respectively), including three major rice pests, Chilo suppressalis (native), Coptotermes formosanus (native), and Pomacea canaliculata (non-native). Cultivation status affected overall diversity, with higher species richness during planting compared to harvesting. No significant differences were observed between native/non-native taxa andbetween cultivation phases. Altogether, we detected a complex environment across trophic levels comprised of both native and non-native agricultural pests using limited sampling effort, demonstrating eDNA analysis as an efficient biomonitoring approach in rice agroecosystems with direct applications for pest, invasive species, and vector surveillance within Taiwan.

This study systematically investigated the physicochemical properties and aroma dynamics of highbush blueberry (Vaccinium corymbosum "Legacy") across five maturity stages (I: green to V: dark blue) using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The fruit diameter, fresh fruit weight, soluble solids content, soluble sugars, and vitamin C content of these blueberries were found to increase significantly with maturation, whereas the firmness and titratable acidity decreased. Seventy-seven volatile organic compounds (VOCs), predominantly consisting of aldehydes (59.09%-85.18%) and alcohols, were identified. The diversity of VOCs decreased from 65 in Maturity Stage I to 38 in Maturity Stage V, although aldehydes such as (E)-2-hexenal (which peaked at 1292.81 μg/kg in Maturity Stage I) remained consistently present across the maturation stages. Orthogonal partial least squares discrimination analysis identified 10 differential volatile metabolites, including (E)-2-hexenal and hexanal, that distinguished between maturity stages. Odor activity values revealed 17 key aroma contributors, notably hexanal (floral), β-myrcene (peppery), and (E)-2-hexenal (green). Fruits at Maturity Stage IV exhibited the most intense aroma and optimal quality, characterized by their light blue peel, high soluble solids content (14.66%), balanced acidity (0.615%), and rich fruity notes. These findings establish distinct volatile signatures for each maturity stage and serve as objective biochemical markers to optimize harvest timing in blueberry cultivation. Furthermore, these stage-specific profiles provide a scientific basis for raw material grading in product processing, guiding the targeted selection of fruits for fresh market distribution or specific processed products such as juices and fermented wines.